Fatal flaws in prediction

The fashionable term now is “Big Data". IBM estimates that we are generating 2.5 quintillion bytes of data each day, more than 90 per cent of which was created in the last two years.

This exponential growth in information is sometimes seen as a cure-all, as computers were in the 1970s. Chris Anderson, the editor of Wired magazine, wrote in 2008 that the sheer volume of data would obviate the need for theory, and even the scientific method.

This is an emphatically pro-science and pro-technology book, and I think of it as a very optimistic one. But it argues that these views are badly mistaken. The numbers have no way of speaking for themselves. We speak for them. We imbue them with meaning. Like Caesar, we may construe them in self-serving ways that are detached from their objective reality.

Data-driven predictions can succeed – and they can fail. It is when we deny our role in the process that the odds of failure rise. Before we demand more of our data, we need to demand more of ourselves.

This attitude might seem surprising if you know my background. I have a reputation for working with data and statistics and using them to make successful predictions. In 2003, bored at a consulting job, I designed a system called PECOTA, which sought to predict the statistics of Major League Baseball players. It contained a number of innovations – its forecasts were probabilistic, for instance, outlining a range of possible outcomes for each player – and we found that it outperformed competing systems when we compared their results. In 2008, I founded the website FiveThirtyEight, which sought to forecast the upcoming election. The FiveThirtyEight forecasts correctly predicted the winner of the presidential contest in 49 of 50 states as well as the winner of all 35 US Senate races.

After the election, I was approached by a number of publishers who wanted to capitalise on the success of books such as Moneyball and Freakonomics that told the story of nerds conquering the world. My book was conceived of along those lines – as an investigation of data-driven predictions in fields ranging from baseball to finance to national security.

But in speaking with well more than 100 experts in more than a dozen fields over the course of four years, reading hundreds of journal articles and books, and travelling everywhere from Las Vegas to Copenhagen in pursuit of my investigation, I came to realise that prediction in the era of Big Data was not going very well. I had been lucky on a few levels: first, in having achieved success despite having made many of the mistakes that I will describe, and second, in having chosen my battles well.

Baseball, for instance, is an exceptional case. It happens to be an especially rich and revealing exception, and the book considers why this is so – why a decade after Moneyball, stat geeks and scouts are now working in harmony.

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The book offers some other hopeful examples. Weather forecasting, which also involves a melding of human judgment and computer power, is one of them. Meteorologists have a bad reputation, but they have made remarkable progress, being able to forecast the landfall position of a hurricane three times more accurately than they were a quarter-century ago. Meanwhile, I met poker players and sports bettors who really were beating Las Vegas, and the computer programmers who built IBM’s Deep Blue and took down a world chess champion.

But these cases of progress in forecasting must be weighed against a series of failures.

If there is one thing that defines Americans – one thing that makes us exceptional – it is our belief in Cassius’s idea that we are in control of our own fates. Our country was founded at the dawn of the Industrial Revolution by religious rebels who had seen that the free flow of ideas had helped to spread not just their religious beliefs, but also those of science and commerce. Most of our strengths and weaknesses as a nation – our ingenuity and our industriousness, our arrogance and our impatience – stem from our unshakable belief in the idea that we choose our own course.

But the new millennium got off to a terrible start for Americans. We had not seen the September 11 attacks coming. The problem was not want of information. As had been the case in the Pearl Harbour attacks six decades earlier, all the signals were there. But we had not put them together. Lacking a theory for how terrorists might behave, we were blind to the data and the attacks were an “unknown unknown" to us.

There also were the widespread failures of prediction that accompanied the recent global financial crisis. Our naive trust in models, and our failure to realise how fragile they were to our choice of assumptions, yielded disastrous results. On a more routine basis, meanwhile, I discovered that we are unable to predict recessions more than a few months in advance, and not for lack of trying. While there has been considerable progress made in controlling inflation, our economic policymakers are otherwise flying blind.

The forecasting models published by political scientists in advance of the 2000 presidential election predicted a landslide 11-point victory for Al Gore. George W. Bush won instead. Rather than being an anomalous result, failures like these have been fairly common in political prediction. A long-term study by Philip Tetlock of the University of Pennsylvania found that when political scientists claimed that a political outcome had absolutely no chance of occurring, it nevertheless happened about 15 per cent of the time. (The political scientists are probably better than TV pundits, however.)

There has recently been, as in the 1970s, a revival of attempts to predict earthquakes, most of them using highly mathematical and data-driven techniques. But these predictions envisaged earthquakes that never happened and failed to prepare us for those that did. The Fukushima nuclear reactor had been designed to handle a magnitude 8.6 earthquake, in part because some seismologists concluded that anything larger was impossible. Then came Japan’s horrible magnitude 9.1 earthquake in March 2011.

There are entire disciplines in which predictions have been failing, often at great cost to society. Consider something like biomedical research. In 2005, an Athens-raised medical researcher named John Ioannidis published a controversial paper titled Why Most Published Research Findings Are False. The paper studied positive findings documented in peer-reviewed journals: descriptions of successful predictions of medical hypotheses carried out in lab experiments. It concluded that most of these findings were likely to fail when applied in the real world. Bayer Laboratories recently confirmed Ioannidis’s hypothesis. They could not replicate about two-thirds of the positive findings claimed in medical journals when they attempted the experiments themselves.

Big Data will produce progress – eventually. How quickly it does, and whether we regress in the meantime, will depend on us.

Fear of the future

Biologically, we are not very different from our ancestors. But some Stone Age strengths are now information-age weaknesses.

Human beings do not have very many natural defences. We are not all that fast, and we are not all that strong. We do not have claws or fangs or body armour. We cannot spit venom. We cannot camouflage ourselves. And we cannot fly. Instead, we survive by means of our wits. Our minds are quick. We are wired to detect patterns and respond to opportunities and threats without much hesitation.

“This need of finding patterns, humans have this more than other animals," I was told by Tomaso Poggio, an MIT neuroscientist who studies how our brains process information. “Recognising objects in difficult situations means generalising. A newborn baby can recognise the basic pattern of a face. It has been learnt by evolution, not by the individual."

The problem, Poggio says, is that these evolutionary instincts sometimes lead us to see patterns when there are none there. “People have been doing that all the time," Poggio said. “Finding patterns in random noise."

The human brain is quite remarkable; it can store perhaps three terabytes of information. And yet that is only about one one-millionth of the information that IBM says is now produced in the world each day. So we have to be terribly selective about the information we choose to remember.

Alvin Toffler, writing in the book Future Shock in 1970, predicted some of the consequences of what he called “information overload". He thought our defence mechanism would be to simplify the world in ways that confirmed our biases, even as the world itself was growing more diverse and more complex.

Our biological instincts are not always very well adapted to the information-rich modern world. Unless we work actively to become aware of the biases we introduce, the returns to additional information may be minimal – or diminishing.

The information overload after the birth of the printing press produced greater sectarianism. Now those different religious ideas could be testified to with more information, more conviction, more “proof" – and less tolerance for dissenting opinion. The same phenomenon seems to be occurring today. Political partisanship began to increase very rapidly in the US at about the time that Toffler wrote Future Shock and it may be accelerating even faster with the advent of the internet.

These partisan beliefs can upset the equation in which more information will bring us closer to the truth. A recent study in Nature found that the more informed that strong political partisans were about global warming, the less they agreed with one another.

Meanwhile, if the quantity of information is increasing by 2.5 quintillion bytes per day, the amount of useful information almost certainly isn’t. Most of it is just noise, and the noise is increasing faster than the signal. There are so many hypotheses to test, so many data sets to mine – but a relatively constant amount of objective truth.

The printing press changed the way in which we made mistakes. Routine errors of transcription became less common. But when there was a mistake, it would be reproduced many times over, as in the case of the Wicked Bible.

Complex systems like the world wide web have this property. They may not fail as often as simpler ones, but when they fail they fail badly.

Capitalism and the internet, both of which are incredibly efficient at propagating information, create the potential for bad ideas as well as good ones to spread. The bad ideas may produce disproportionate effects. In advance of the financial crisis, the system was so highly levered that a single lax assumption in the credit ratings agencies’ models played a huge role in bringing down the whole global financial system.

Regulation is one approach to solving these problems. But I am suspicious that it is an excuse to avoid looking within ourselves for answers. We need to stop, and admit it: we have a prediction problem. We love to predict things – and we aren’t very good at it.